Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations1528
Missing cells0
Missing cells (%)0.0%
Duplicate rows204
Duplicate rows (%)13.4%
Total size in memory167.1 KiB
Average record size in memory112.0 B

Variable types

Categorical1
Numeric12

Alerts

type has constant value "Syrah"Constant
Dataset has 204 (13.4%) duplicate rowsDuplicates
citric acid is highly overall correlated with fixed acidity and 2 other fieldsHigh correlation
density is highly overall correlated with fixed acidityHigh correlation
fixed acidity is highly overall correlated with citric acid and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
pH is highly overall correlated with citric acid and 1 other fieldsHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation
volatile acidity is highly overall correlated with citric acidHigh correlation
citric acid has 130 (8.5%) zerosZeros

Reproduction

Analysis started2024-11-01 20:35:57.301109
Analysis finished2024-11-01 20:36:15.132176
Duration17.83 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

type
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.9 KiB
Syrah
1528 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters7640
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSyrah
2nd rowSyrah
3rd rowSyrah
4th rowSyrah
5th rowSyrah

Common Values

ValueCountFrequency (%)
Syrah 1528
100.0%

Length

2024-11-01T17:36:15.317787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-01T17:36:15.436841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
syrah 1528
100.0%

Most occurring characters

ValueCountFrequency (%)
S 1528
20.0%
y 1528
20.0%
r 1528
20.0%
a 1528
20.0%
h 1528
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1528
20.0%
y 1528
20.0%
r 1528
20.0%
a 1528
20.0%
h 1528
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1528
20.0%
y 1528
20.0%
r 1528
20.0%
a 1528
20.0%
h 1528
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1528
20.0%
y 1528
20.0%
r 1528
20.0%
a 1528
20.0%
h 1528
20.0%

fixed acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1712696
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:15.564167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.1
Q17.1
median7.8
Q39
95-th percentile11.3
Maximum15.9
Range11.3
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.5686451
Coefficient of variation (CV)0.19197079
Kurtosis0.62203093
Mean8.1712696
Median Absolute Deviation (MAD)0.9
Skewness0.79997273
Sum12485.7
Variance2.4606475
MonotonicityNot monotonic
2024-11-01T17:36:15.731779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 67
 
4.4%
7.1 57
 
3.7%
7.8 53
 
3.5%
7.5 52
 
3.4%
7 50
 
3.3%
7.7 49
 
3.2%
7.6 46
 
3.0%
6.8 46
 
3.0%
8.2 44
 
2.9%
7.4 43
 
2.8%
Other values (76) 1021
66.8%
ValueCountFrequency (%)
4.6 1
 
0.1%
4.7 1
 
0.1%
4.9 1
 
0.1%
5 6
0.4%
5.1 4
 
0.3%
5.2 6
0.4%
5.3 3
 
0.2%
5.4 5
 
0.3%
5.5 1
 
0.1%
5.6 14
0.9%
ValueCountFrequency (%)
15.9 1
 
0.1%
13.8 1
 
0.1%
13.3 1
 
0.1%
13 1
 
0.1%
12.8 3
0.2%
12.7 4
0.3%
12.6 4
0.3%
12.5 7
0.5%
12.4 3
0.2%
12.3 1
 
0.1%

volatile acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct142
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52899869
Minimum0.12
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:15.884501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.27
Q10.39
median0.52
Q30.64
95-th percentile0.84
Maximum1.58
Range1.46
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.18036784
Coefficient of variation (CV)0.34096085
Kurtosis1.2175933
Mean0.52899869
Median Absolute Deviation (MAD)0.12
Skewness0.66953995
Sum808.31
Variance0.032532559
MonotonicityNot monotonic
2024-11-01T17:36:16.035622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 47
 
3.1%
0.43 42
 
2.7%
0.5 40
 
2.6%
0.36 38
 
2.5%
0.4 37
 
2.4%
0.58 37
 
2.4%
0.59 36
 
2.4%
0.56 34
 
2.2%
0.52 33
 
2.2%
0.39 32
 
2.1%
Other values (132) 1152
75.4%
ValueCountFrequency (%)
0.12 3
 
0.2%
0.16 2
 
0.1%
0.18 10
0.7%
0.19 2
 
0.1%
0.2 3
 
0.2%
0.21 4
 
0.3%
0.22 6
0.4%
0.23 5
 
0.3%
0.24 13
0.9%
0.25 7
0.5%
ValueCountFrequency (%)
1.58 1
 
0.1%
1.33 2
0.1%
1.24 1
 
0.1%
1.185 1
 
0.1%
1.18 1
 
0.1%
1.13 1
 
0.1%
1.115 1
 
0.1%
1.09 1
 
0.1%
1.07 1
 
0.1%
1.04 3
0.2%

citric acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2627356
Minimum0
Maximum1
Zeros130
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:16.205427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.25
Q30.41
95-th percentile0.59
Maximum1
Range1
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.19125745
Coefficient of variation (CV)0.72794647
Kurtosis-0.73918329
Mean0.2627356
Median Absolute Deviation (MAD)0.16
Skewness0.35099701
Sum401.46
Variance0.036579414
MonotonicityNot monotonic
2024-11-01T17:36:16.342448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
 
8.5%
0.49 54
 
3.5%
0.24 50
 
3.3%
0.02 49
 
3.2%
0.26 38
 
2.5%
0.1 35
 
2.3%
0.01 33
 
2.2%
0.08 33
 
2.2%
0.21 32
 
2.1%
0.32 32
 
2.1%
Other values (68) 1042
68.2%
ValueCountFrequency (%)
0 130
8.5%
0.01 33
 
2.2%
0.02 49
 
3.2%
0.03 30
 
2.0%
0.04 29
 
1.9%
0.05 20
 
1.3%
0.06 24
 
1.6%
0.07 20
 
1.3%
0.08 33
 
2.2%
0.09 30
 
2.0%
ValueCountFrequency (%)
1 1
 
0.1%
0.78 1
 
0.1%
0.76 1
 
0.1%
0.75 1
 
0.1%
0.74 3
0.2%
0.73 3
0.2%
0.72 1
 
0.1%
0.71 1
 
0.1%
0.7 2
0.1%
0.69 4
0.3%

residual sugar
Real number (ℝ)

Distinct86
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4559882
Minimum0.9
Maximum13.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:16.471318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.5
Q11.9
median2.2
Q32.5
95-th percentile4.6
Maximum13.9
Range13
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation1.1849188
Coefficient of variation (CV)0.48246111
Kurtosis22.244522
Mean2.4559882
Median Absolute Deviation (MAD)0.3
Skewness3.9434375
Sum3752.75
Variance1.4040326
MonotonicityNot monotonic
2024-11-01T17:36:16.604675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 156
 
10.2%
1.8 128
 
8.4%
2.1 128
 
8.4%
2.2 122
 
8.0%
1.9 116
 
7.6%
2.3 107
 
7.0%
2.4 86
 
5.6%
2.5 82
 
5.4%
2.6 77
 
5.0%
1.7 76
 
5.0%
Other values (76) 450
29.5%
ValueCountFrequency (%)
0.9 2
 
0.1%
1.2 8
 
0.5%
1.3 5
 
0.3%
1.4 33
 
2.2%
1.5 30
 
2.0%
1.6 58
3.8%
1.65 2
 
0.1%
1.7 76
5.0%
1.75 2
 
0.1%
1.8 128
8.4%
ValueCountFrequency (%)
13.9 1
 
0.1%
13.4 1
 
0.1%
12.9 1
 
0.1%
10.7 1
 
0.1%
9 1
 
0.1%
8.9 1
 
0.1%
8.8 2
0.1%
8.6 1
 
0.1%
8.3 3
0.2%
8.1 2
0.1%

chlorides
Real number (ℝ)

Distinct152
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087124346
Minimum0.012
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:16.736351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.05335
Q10.07
median0.079
Q30.09
95-th percentile0.12565
Maximum0.611
Range0.599
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.047637908
Coefficient of variation (CV)0.54678067
Kurtosis41.641523
Mean0.087124346
Median Absolute Deviation (MAD)0.01
Skewness5.709874
Sum133.126
Variance0.0022693703
MonotonicityNot monotonic
2024-11-01T17:36:16.861955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 65
 
4.3%
0.074 53
 
3.5%
0.076 51
 
3.3%
0.078 50
 
3.3%
0.084 49
 
3.2%
0.077 47
 
3.1%
0.082 44
 
2.9%
0.079 43
 
2.8%
0.071 43
 
2.8%
0.075 42
 
2.7%
Other values (142) 1041
68.1%
ValueCountFrequency (%)
0.012 2
 
0.1%
0.034 1
 
0.1%
0.038 2
 
0.1%
0.039 4
0.3%
0.041 4
0.3%
0.042 3
0.2%
0.043 1
 
0.1%
0.044 5
0.3%
0.045 4
0.3%
0.046 4
0.3%
ValueCountFrequency (%)
0.611 1
 
0.1%
0.61 1
 
0.1%
0.467 1
 
0.1%
0.464 1
 
0.1%
0.422 1
 
0.1%
0.415 3
0.2%
0.414 2
0.1%
0.413 1
 
0.1%
0.403 1
 
0.1%
0.401 1
 
0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.843586
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:16.987745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median14
Q321
95-th percentile35
Maximum72
Range71
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.34505
Coefficient of variation (CV)0.65294873
Kurtosis1.9583288
Mean15.843586
Median Absolute Deviation (MAD)7
Skewness1.2162433
Sum24209
Variance107.02005
MonotonicityNot monotonic
2024-11-01T17:36:17.128067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 126
 
8.2%
5 99
 
6.5%
15 77
 
5.0%
12 71
 
4.6%
10 69
 
4.5%
7 67
 
4.4%
9 61
 
4.0%
16 59
 
3.9%
11 58
 
3.8%
17 56
 
3.7%
Other values (48) 785
51.4%
ValueCountFrequency (%)
1 3
 
0.2%
2 1
 
0.1%
3 49
 
3.2%
4 40
 
2.6%
5 99
6.5%
5.5 1
 
0.1%
6 126
8.2%
7 67
4.4%
8 56
3.7%
9 61
4.0%
ValueCountFrequency (%)
72 1
 
0.1%
68 2
0.1%
66 1
 
0.1%
57 1
 
0.1%
53 1
 
0.1%
52 3
0.2%
51 4
0.3%
50 2
0.1%
48 2
0.1%
47 1
 
0.1%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.28534
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:17.256633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q122
median37
Q362
95-th percentile113
Maximum289
Range283
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.101253
Coefficient of variation (CV)0.7151563
Kurtosis3.910232
Mean46.28534
Median Absolute Deviation (MAD)18
Skewness1.5417693
Sum70724
Variance1095.6929
MonotonicityNot monotonic
2024-11-01T17:36:17.382741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 41
 
2.7%
15 34
 
2.2%
24 33
 
2.2%
18 33
 
2.2%
20 33
 
2.2%
31 32
 
2.1%
14 31
 
2.0%
38 30
 
2.0%
23 30
 
2.0%
19 29
 
1.9%
Other values (134) 1202
78.7%
ValueCountFrequency (%)
6 3
 
0.2%
7 4
 
0.3%
8 14
0.9%
9 14
0.9%
10 27
1.8%
11 26
1.7%
12 29
1.9%
13 28
1.8%
14 31
2.0%
15 34
2.2%
ValueCountFrequency (%)
289 1
0.1%
278 1
0.1%
165 1
0.1%
160 1
0.1%
155 1
0.1%
153 1
0.1%
152 1
0.1%
151 2
0.1%
149 1
0.1%
148 2
0.1%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct414
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99656575
Minimum0.99007
Maximum1.001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:17.517382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.99358
Q10.99554
median0.99664
Q30.9976175
95-th percentile0.9993865
Maximum1.001
Range0.01093
Interquartile range (IQR)0.0020775

Descriptive statistics

Standard deviation0.0016850113
Coefficient of variation (CV)0.001690818
Kurtosis0.57389569
Mean0.99656575
Median Absolute Deviation (MAD)0.00106
Skewness-0.40700877
Sum1522.7525
Variance2.8392631 × 10-6
MonotonicityNot monotonic
2024-11-01T17:36:17.647198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9972 36
 
2.4%
0.9968 35
 
2.3%
0.9976 35
 
2.3%
0.998 29
 
1.9%
0.9962 28
 
1.8%
0.9978 26
 
1.7%
0.9964 25
 
1.6%
0.997 24
 
1.6%
0.9994 24
 
1.6%
0.9982 23
 
1.5%
Other values (404) 1243
81.3%
ValueCountFrequency (%)
0.99007 2
0.1%
0.9902 1
0.1%
0.99064 2
0.1%
0.9908 1
0.1%
0.99084 1
0.1%
0.9912 1
0.1%
0.9915 1
0.1%
0.99154 1
0.1%
0.99157 1
0.1%
0.9916 2
0.1%
ValueCountFrequency (%)
1.001 6
0.4%
1 10
0.7%
0.9999 1
 
0.1%
0.9998 10
0.7%
0.99976 1
 
0.1%
0.99975 1
 
0.1%
0.99974 1
 
0.1%
0.9997 8
0.5%
0.99965 1
 
0.1%
0.9996 12
0.8%

pH
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3169568
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:17.987760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.08
Q13.22
median3.32
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.15190721
Coefficient of variation (CV)0.045797164
Kurtosis0.92150592
Mean3.3169568
Median Absolute Deviation (MAD)0.09
Skewness0.21457146
Sum5068.31
Variance0.023075802
MonotonicityNot monotonic
2024-11-01T17:36:18.124151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.36 56
 
3.7%
3.3 54
 
3.5%
3.26 51
 
3.3%
3.39 48
 
3.1%
3.38 48
 
3.1%
3.29 45
 
2.9%
3.34 43
 
2.8%
3.28 41
 
2.7%
3.32 40
 
2.6%
3.35 39
 
2.6%
Other values (77) 1063
69.6%
ValueCountFrequency (%)
2.74 1
 
0.1%
2.87 1
 
0.1%
2.88 2
0.1%
2.89 4
0.3%
2.9 1
 
0.1%
2.92 1
 
0.1%
2.93 3
0.2%
2.94 4
0.3%
2.98 4
0.3%
2.99 2
0.1%
ValueCountFrequency (%)
4.01 2
0.1%
3.9 2
0.1%
3.85 1
 
0.1%
3.78 2
0.1%
3.75 1
 
0.1%
3.74 1
 
0.1%
3.72 3
0.2%
3.71 4
0.3%
3.7 1
 
0.1%
3.69 4
0.3%

sulphates
Real number (ℝ)

Distinct94
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6560733
Minimum0.33
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:18.255116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.47
Q10.55
median0.62
Q30.7225
95-th percentile0.93
Maximum2
Range1.67
Interquartile range (IQR)0.1725

Descriptive statistics

Standard deviation0.17047151
Coefficient of variation (CV)0.25983608
Kurtosis12.083571
Mean0.6560733
Median Absolute Deviation (MAD)0.08
Skewness2.496377
Sum1002.48
Variance0.029060537
MonotonicityNot monotonic
2024-11-01T17:36:18.388699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 68
 
4.5%
0.58 67
 
4.4%
0.54 67
 
4.4%
0.62 61
 
4.0%
0.56 56
 
3.7%
0.57 54
 
3.5%
0.59 51
 
3.3%
0.55 50
 
3.3%
0.53 49
 
3.2%
0.63 48
 
3.1%
Other values (84) 957
62.6%
ValueCountFrequency (%)
0.33 1
 
0.1%
0.37 2
 
0.1%
0.39 6
 
0.4%
0.4 4
 
0.3%
0.42 5
 
0.3%
0.43 8
0.5%
0.44 14
0.9%
0.45 12
0.8%
0.46 18
1.2%
0.47 19
1.2%
ValueCountFrequency (%)
2 1
 
0.1%
1.98 1
 
0.1%
1.95 2
0.1%
1.62 1
 
0.1%
1.61 1
 
0.1%
1.59 1
 
0.1%
1.56 1
 
0.1%
1.36 3
0.2%
1.34 1
 
0.1%
1.33 1
 
0.1%

alcohol
Real number (ℝ)

Distinct59
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.433213
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:18.532078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311.1
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.0608213
Coefficient of variation (CV)0.10167733
Kurtosis0.1986475
Mean10.433213
Median Absolute Deviation (MAD)0.7
Skewness0.86148933
Sum15941.95
Variance1.1253418
MonotonicityNot monotonic
2024-11-01T17:36:18.669731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 134
 
8.8%
9.4 101
 
6.6%
9.8 75
 
4.9%
10.5 67
 
4.4%
9.2 67
 
4.4%
10 62
 
4.1%
9.6 57
 
3.7%
9.3 56
 
3.7%
11 56
 
3.7%
9.7 51
 
3.3%
Other values (49) 802
52.5%
ValueCountFrequency (%)
8.4 1
 
0.1%
8.5 1
 
0.1%
8.7 2
 
0.1%
9 23
 
1.5%
9.05 1
 
0.1%
9.1 23
 
1.5%
9.2 67
4.4%
9.25 1
 
0.1%
9.3 56
3.7%
9.4 101
6.6%
ValueCountFrequency (%)
14.9 1
 
0.1%
14 7
0.5%
13.6 4
0.3%
13.5 1
 
0.1%
13.4 3
 
0.2%
13.3 3
 
0.2%
13.2 1
 
0.1%
13.1 2
 
0.1%
13 4
0.3%
12.9 9
0.6%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6341623
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-11-01T17:36:18.779785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80863736
Coefficient of variation (CV)0.14352397
Kurtosis0.28817409
Mean5.6341623
Median Absolute Deviation (MAD)1
Skewness0.23770656
Sum8609
Variance0.65389439
MonotonicityNot monotonic
2024-11-01T17:36:18.875856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 653
42.7%
6 607
39.7%
7 189
 
12.4%
4 52
 
3.4%
8 18
 
1.2%
3 9
 
0.6%
ValueCountFrequency (%)
3 9
 
0.6%
4 52
 
3.4%
5 653
42.7%
6 607
39.7%
7 189
 
12.4%
8 18
 
1.2%
ValueCountFrequency (%)
8 18
 
1.2%
7 189
 
12.4%
6 607
39.7%
5 653
42.7%
4 52
 
3.4%
3 9
 
0.6%

Interactions

2024-11-01T17:36:13.384112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:57.638632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:58.918286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:00.370736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:01.642271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:03.137354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:04.546302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:06.038291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:07.621596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:08.970490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:10.352836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:11.885299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:13.485137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:57.745969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:59.022031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:00.477819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:01.748756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:03.237325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:04.650734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:06.145431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:07.744499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:09.077742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:10.516757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:11.984069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:13.597108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:57.854376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:59.136597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:00.585658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:01.854681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:03.350356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:04.774473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:06.258780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:07.869539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:09.189943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:10.668639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:12.098600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:13.696952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:57.969664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:59.242598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:00.693910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:01.963146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:03.455886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:04.884307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:06.371598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:07.997487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:09.292847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:10.798161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:12.388110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:13.802041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:58.070482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:59.352769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:00.803698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:02.116341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:03.572255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:04.996324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:06.486950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:08.107358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:09.397980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:10.931310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:12.494807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:13.909728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:58.171678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:59.467583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:00.904254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:02.222341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:03.676442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:05.131700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:06.605915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:08.216541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:09.503243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:11.053859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:12.600213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:14.020264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:58.282933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:59.583946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:01.006187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:02.333716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:03.785974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:05.270781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:06.721098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:08.330922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:09.621033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:11.186048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:12.717941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:14.127161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:58.388660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:59.700025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:01.113388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:02.441639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:03.915820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:05.391407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:06.829098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:08.438894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:09.745524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:11.316186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:12.832242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:14.238572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:58.496140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:59.919469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:01.218830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:02.562793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:04.048560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:05.506483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:06.938790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:08.537893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:09.865423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:11.443944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:12.932651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:14.367061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:58.605489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:00.036855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:01.322565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:02.684375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:04.182452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:05.612029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:07.204687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:08.646931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:09.977924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:11.551552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:13.039195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:14.502598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:58.714857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:00.152691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:01.432137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:02.808444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:04.307837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:05.745826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:07.359652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:08.751964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:10.101637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:11.668375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:13.154457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:14.635681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:35:58.816698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:00.264124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:01.536166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:02.912550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:04.433868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:05.925722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:07.488641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:08.864729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:10.235573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:11.778094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-01T17:36:13.279912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-01T17:36:18.952257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
alcoholchloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidevolatile acidity
alcohol1.000-0.2960.106-0.482-0.058-0.0800.1740.4890.1290.213-0.268-0.241
chlorides-0.2961.0000.0940.4050.235-0.010-0.225-0.1880.1930.0090.1250.158
citric acid0.1060.0941.0000.3110.642-0.060-0.5290.2200.1480.3370.018-0.622
density-0.4820.4050.3111.0000.584-0.041-0.275-0.1930.3800.1460.1280.040
fixed acidity-0.0580.2350.6420.5841.000-0.173-0.6970.1180.1780.203-0.094-0.275
free sulfur dioxide-0.080-0.010-0.060-0.041-0.1731.0000.105-0.0550.0690.0350.7900.010
pH0.174-0.225-0.529-0.275-0.6970.1051.000-0.044-0.062-0.074-0.0180.230
quality0.489-0.1880.220-0.1930.118-0.055-0.0441.0000.0320.378-0.207-0.390
residual sugar0.1290.1930.1480.3800.1780.069-0.0620.0321.0000.0120.1310.033
sulphates0.2130.0090.3370.1460.2030.035-0.0740.3780.0121.000-0.016-0.337
total sulfur dioxide-0.2680.1250.0180.128-0.0940.790-0.018-0.2070.131-0.0161.0000.085
volatile acidity-0.2410.158-0.6220.040-0.2750.0100.230-0.3900.033-0.3370.0851.000

Missing values

2024-11-01T17:36:14.811890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-01T17:36:15.034136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
1632Syrah7.40.700.001.90.07611.034.00.99783.510.569.45
1633Syrah7.80.880.002.60.09825.067.00.99683.200.689.85
1634Syrah7.80.760.042.30.09215.054.00.99703.260.659.85
1635Syrah11.20.280.561.90.07517.060.00.99803.160.589.86
1636Syrah7.40.700.001.90.07611.034.00.99783.510.569.45
1637Syrah7.40.660.001.80.07513.040.00.99783.510.569.45
1638Syrah7.90.600.061.60.06915.059.00.99643.300.469.45
1639Syrah7.30.650.001.20.06515.021.00.99463.390.4710.07
1640Syrah7.80.580.022.00.0739.018.00.99683.360.579.57
1641Syrah7.50.500.366.10.07117.0102.00.99783.350.8010.55
typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
3221Syrah6.60.7250.207.80.07329.079.00.997703.290.549.25
3222Syrah6.30.5500.151.80.07726.035.00.993143.320.8211.66
3223Syrah5.40.7400.091.70.08916.026.00.994023.670.5611.66
3224Syrah6.30.5100.132.30.07629.040.00.995743.420.7511.06
3225Syrah6.80.6200.081.90.06828.038.00.996513.420.829.56
3226Syrah6.20.6000.082.00.09032.044.00.994903.450.5810.55
3227Syrah5.90.5500.102.20.06239.051.00.995123.520.7611.26
3228Syrah6.30.5100.132.30.07629.040.00.995743.420.7511.06
3229Syrah5.90.6450.122.00.07532.044.00.995473.570.7110.25
3230Syrah6.00.3100.473.60.06718.042.00.995493.390.6611.06

Duplicate rows

Most frequently occurring

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
22Syrah6.70.4600.241.70.07718.034.00.994803.390.6010.664
52Syrah7.20.3600.462.10.07424.044.00.995343.400.8511.074
63Syrah7.20.6950.132.00.07612.020.00.995463.290.5410.154
81Syrah7.50.5100.021.70.08413.031.00.995383.360.5410.564
5Syrah6.00.5000.001.40.05715.026.00.994483.360.459.553
12Syrah6.40.6400.211.80.08114.031.00.996893.590.669.853
39Syrah7.00.6500.022.10.0668.025.00.997203.470.679.563
40Syrah7.00.6900.072.50.09115.021.00.995723.380.6011.363
60Syrah7.20.6300.001.90.09714.038.00.996753.370.589.063
104Syrah7.80.6000.262.00.08031.0131.00.996223.210.529.953